• Why ggplot2?
• Conceptual ggplot2
• Basic ggplot2 syntax
• Common pitfalls
• The power of layers
• The power of groups
• The power of facets
• Scales and legends
• Themes and refinements
• Extensions

All source code and slides at https://github.com/rladies/meetup-presentations_rtp/tree/master/2019-03-06-ggplot2

• Datasets for today are here: https://www.dropbox.com/s/zhmn02ti0ggxdj7/rladies_ggplot2_datasets.rda?dl=1

• You can download them from R:

  download.file( 'https://www.dropbox.com/s/zhmn02ti0ggxdj7/ rladies_ggplot2_datasets.rda?dl=1', 'rladies_ggplot2_datasets.rda')

  attach('rladies_ggplot2_datasets.rda')

• Transit ride data
  • rides_df: one row per ride
  • daily_df: daily summary of rides
  • hourly_df: hourly summary of rides
• Durham registered voter data
  • durham_voters_df: one row per voter

• explore data very quickly, and don't screw up
• graphics "pit of success"
• refine plots to be publication quality
• see discussions of ggplot2 vs base R
  • http://nssdeviations.com/episode-12-the-new-bayesian-vs-frequentist

• based on The Grammar of Graphics by L. Wilkinson and others
• principles and patterns
• very complex plots from simple building blocks
  • https://vita.had.co.nz/papers/layered-grammar.pdf

• the most prominent static graphics package in R
• help is easy to find
• developers are contibuting extensions

• map variables to aethestics
• add "geoms" for visual representation layers
• scales can be independently managed
• legends are automatically created
• statistics are sometimes calculated by geoms…

• See the ggplot2 book by H. Wickham
• See the R for Data Science book

• DATA
• MAPPING
• GEOM

• A geom is required. A stat (i.e., statistical tranformation) is not required.
• We won't cover stat objects today…just letting you know they exist.

For example: geom_bar() uses stat_count as it's default stat.

Run this only if it's your first time using tidyverse on your computer.

install.packages("tidyverse")

library("tidyverse")

download.file(
'https://www.dropbox.com/s/zhmn02ti0ggxdj7/rladies_ggplot2_datasets.rda?dl=1',
'rladies_ggplot2_datasets.rda')
        
attach('rladies_ggplot2_datasets.rda')

# or download from dropbox link and then run
# load("<your path to the .rda file>")

ggplot(data = daily_df) +
geom_point(mapping = aes(x = ride_date, y = n_rides))

ggplot(data = daily_df, mapping = aes(x = ride_date, y = n_rides)) +
geom_point()

ggplot(daily_df, aes(x = ride_date, y = n_rides)) +
geom_point()

daily_df %>%
ggplot(aes(x = ride_date, y = n_rides)) +
geom_point()

# How else could we write this (using data = )?

hourly_df %>%
ggplot(aes(x = hour, y = n_rides)) +
geom_point()

# Color by days of the week.
daily_df %>%
ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
geom_point()

# Size by number of riders.
daily_df %>%
ggplot(aes(x = ride_date, y = n_rides, size = n_riders)) +
geom_point()

# Color by weekend or not.
daily_df %>%
ggplot(aes(x = ride_date, y = n_rides, 
           color = day_of_week %in% c('Sat', 'Sun'))) +
geom_point()

# Color by day type.
daily_df %>%
  mutate(day_type = if_else(day_of_week %in% c('Sat', 'Sun'),
                            'Weekend',
                            'Weekday')) %>%
ggplot(aes(x = ride_date, y = n_rides, color = day_type)) +
geom_point()

daily_df %>%
ggplot() +
geom_point(aes(x = ride_date, y = n_rides, color = 'blue'))

daily_df %>%
ggplot() +
geom_point(aes(x = ride_date, y = n_rides), color = 'blue')

daily_df %>%
mutate(day_type = if_else(day_of_week %in% c('Sat', 'Sun'), 'Weekend', 'Weekday')) %>%

ggplot(aes(x = ride_date, y = n_rides, color = day_type)) %>%

geom_point()

Plot the number of unique routes per day over time, colored by day of week. (n_unique_routes)

daily_df %>%
  ggplot(aes(x = ride_date, y = n_unique_routes, color = day_of_week)) +
  geom_point() 

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides)) +
  geom_point() 

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides)) +
  geom_point() +
  geom_line()

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides)) +
  geom_point() + 
  geom_smooth(span = .1) # try changing span

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() + 
  geom_line()

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_smooth(span = .2, se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point(data = filter(daily_df,
                           !(day_of_week %in% c('Sat', 'Sun')) 
                           & n_rides < 200),
             size = 5, color = 'gray') +
  geom_point()

There are a number of other geoms besides geom_point(), geom_line(), geom_smooth(), and geom_text().

More info: https://ggplot2.tidyverse.org/reference/

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides)) +
  geom_point() + 
  geom_line() +
  facet_wrap( ~ day_of_week)

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_smooth(span = .2, se = FALSE) +
  xlab('') +
  ylab('# of Transit Rides') +
  ggtitle('Transit Rides over time by Day of Week') +
  scale_color_discrete('Day of Week')

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  scale_y_reverse()

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  scale_y_sqrt()

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  scale_y_continuous(breaks = c(0, 200, 500))

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  theme_bw()

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  theme_dark()

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  theme(axis.text.x = element_text(angle = 90))

To really master themes:

http://docs.ggplot2.org/dev/vignettes/themes.html

Better than the regular documentation:

http://docs.ggplot2.org/current/

Don't forget the ggplot2 cheatsheet!

https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

Examples of layering

https://evamaerey.github.io/ggplot_flipbook/ggplot_flipbook_xaringan.html#1

Check it out!

http://www.ggplot2-exts.org/

Wednesday, March 20th, 6pm

• Location: +TBD, near South Durham
• Topic: +#tidytuesday Work Group

Topic/format suggestions?

Thanks to Elaine McVey for sharing her slides from a previous R-Ladies RTP meetup!